Google's refreshed crash course mixes short videos, interactive visualizations, and quizzes across linear and logistic regression, data handling, neural networks, embeddings, LLMs, and production ML. Ideal for engineers who want a practical, fast overview before committing to a longer program.
Aspiring data scientists and ML engineers. This is a beginner-level course in Machine Learning, running about ~15 hours (12 modules).
Free. No payment required at any point.
A micro-course that gets you from zero to your first random forest model and Kaggle competition submission in an afternoon. Perfect for people who learn by doing and want immediate, runnable code.
Microsoft Cloud Advocates' open curriculum walks through regression, classification, clustering, NLP, time series, and RL with Scikit-learn, using real-world datasets from around the globe. A great structured syllabus for self-learners and study groups who prefer notebooks over videos.
Andrew Ng's updated three-course sequence covers regression, classification, neural networks, decision trees, clustering, and recommender systems in Python. Take it as the canonical first ML course if you want a rigorous but gentle on-ramp with hands-on labs.